Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

Yujin Liu¹, Zhenkai Zhang¹, Li Ma¹, Yan Jia^1,2,*, Weihua Yin³, Zhifeng Liu³

Energy Engineering, Vol.121, No.10, pp. 3019-3035, 2024, DOI:10.32604/ee.2024.052594 - 11 September 2024

Abstract Accurate short-term photovoltaic (PV) power prediction helps to improve the economic efficiency of power stations and is of great significance to the arrangement of grid scheduling plans. In order to improve the accuracy of PV power prediction further, this paper proposes a data cleaning method combining density clustering and support vector machine. It constructs a short-term PV power prediction model based on particle swarm optimization (PSO) optimized Long Short-Term Memory (LSTM) network. Firstly, the input features are determined using Pearson’s correlation coefficient. The feature information is clustered using density-based spatial clustering of applications with noise More >

Jiawen Li^1,2, Yuesheng Huang¹, Yayi Lu¹, Leijun Wang^1,*, Yongqi Ren¹, Rongjun Chen¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1581-1599, 2024, DOI:10.32604/cmc.2024.052666 - 18 July 2024

Abstract In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model… More >

Hao Bai¹, Beiyuan Liu^2,*, Hongwen Liu³, Jupeng Zeng², Jian Ouyang⁴, Yipeng Liu¹

Energy Engineering, Vol.121, No.8, pp. 2191-2211, 2024, DOI:10.32604/ee.2024.049848 - 19 July 2024

Abstract Most ground faults in distribution network are caused by insulation deterioration of power equipment. It is difficult to find the insulation deterioration of the distribution network in time, and the development trend of the initial insulation fault is unknown, which brings difficulties to the distribution inspection. In order to solve the above problems, a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed. Firstly, the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network, and the… More >

Siti Aekbal Salleh^1,2,*, Nafisah Khalid¹, Natasha Danny⁶, Nurul Ain Mohd. Zaki^2,3, Mustafa Ustuner⁴, Zulkiflee Abd Latif^1,2, Vladimir Foronda⁵

Revue Internationale de Géomatique, Vol.33, pp. 183-199, 2024, DOI:10.32604/rig.2024.050723 - 27 June 2024

Abstract Due to the spectral and spatial properties of pervious and impervious surfaces, image classification and information extraction in detailed, small-scale mapping of urban surface materials is quite difficult and complex. Emerging methods and innovations in image classification have centred on object-based classification techniques and various segmentation techniques, which are fundamental to this approach. Consequently, the purpose of this study is to determine which classification method is most suitable for extracting linear features in terms of techniques and performance by comparing two classification methods, pixel-based approach and object-based approach, using WorldView-2 satellite imagery to specifically highlight… More > Graphic Abstract

Naif Al Mudawi^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4945-4962, 2024, DOI:10.32604/cmc.2024.048967 - 20 June 2024

Abstract Parkinson’s disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient’s symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply… More >

Widad Elbakri¹, Maheyzah Md. Siraj^1,*, Bander Ali Saleh Al-rimy¹, Sultan Noman Qasem², Tawfik Al-Hadhrami³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3725-3756, 2024, DOI:10.32604/cmc.2024.048105 - 20 June 2024

Abstract Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, are increasingly targeted by malicious actors. These threats encompass unauthorized access, data breaches, denial-of-service attacks, and evolving malware variants. Traditional security solutions often struggle with the dynamic nature of cloud environments, highlighting the need for robust Adaptive Cloud Intrusion Detection Systems (CIDS). Existing adaptive CIDS solutions, while offering improved detection capabilities, often face limitations such as reliance on approximations for change point detection, hindering their precision in identifying anomalies. This can lead to missed attacks or an abundance of false alarms, impacting overall… More >

Bo Zhu^*, Xiaona Jing, Lan Qiu, Runbo Li

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3977-3999, 2024, DOI:10.32604/cmc.2024.048062 - 20 June 2024

Abstract When building a classification model, the scenario where the samples of one class are significantly more than those of the other class is called data imbalance. Data imbalance causes the trained classification model to be in favor of the majority class (usually defined as the negative class), which may do harm to the accuracy of the minority class (usually defined as the positive class), and then lead to poor overall performance of the model. A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article, which is based on a new hybrid… More >

Chuandong Qin^1,2, Yu Cao^1,*, Liqun Meng¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228 - 15 May 2024

Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is… More >

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672 - 19 March 2024

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree More >